Overview

Dataset statistics

Number of variables39
Number of observations7913
Missing cells75209
Missing cells (%)24.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory312.0 B

Variable types

Text11
DateTime6
Categorical13
Numeric6
Unsupported3

Alerts

Printing Date has constant value ""Constant
Template has constant value ""Constant
Printed has constant value ""Constant
PR Acceptance Date has constant value ""Constant
ReOpened is highly imbalanced (53.2%)Imbalance
Location Code is highly imbalanced (50.3%)Imbalance
Plant No_ is highly imbalanced (52.2%)Imbalance
PR Item Category is highly imbalanced (98.0%)Imbalance
PR Type is highly imbalanced (90.4%)Imbalance
Department has 252 (3.2%) missing valuesMissing
Intended For has 268 (3.4%) missing valuesMissing
Phase of Work has 4907 (62.0%) missing valuesMissing
Reference has 5720 (72.3%) missing valuesMissing
Work Description has 5568 (70.4%) missing valuesMissing
Batch has 6943 (87.7%) missing valuesMissing
External PR No_ has 7913 (100.0%) missing valuesMissing
Remarks has 1065 (13.5%) missing valuesMissing
Purchaser Code has 903 (11.4%) missing valuesMissing
Location Code has 7858 (99.3%) missing valuesMissing
Equipment No_ has 578 (7.3%) missing valuesMissing
Reason for Cancellation has 7876 (99.5%) missing valuesMissing
User ID has 2559 (32.3%) missing valuesMissing
MP Work Order No_ has 7913 (100.0%) missing valuesMissing
Reference MP PR No_ has 6973 (88.1%) missing valuesMissing
No_ Series has 7913 (100.0%) missing valuesMissing
timestamp has unique valuesUnique
No_ has unique valuesUnique
External PR No_ is an unsupported type, check if it needs cleaning or further analysisUnsupported
MP Work Order No_ is an unsupported type, check if it needs cleaning or further analysisUnsupported
No_ Series is an unsupported type, check if it needs cleaning or further analysisUnsupported
Dimension Set ID has 2691 (34.0%) zerosZeros
PR Monitoring Status has 126 (1.6%) zerosZeros
Priority has 440 (5.6%) zerosZeros
Role Center Status has 3864 (48.8%) zerosZeros
PR Approving Status has 7722 (97.6%) zerosZeros

Reproduction

Analysis started2023-10-11 01:49:34.581335
Analysis finished2023-10-11 01:49:40.358419
Duration5.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

timestamp
Text

UNIQUE 

Distinct7913
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
2023-10-11T09:49:40.644599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters126608
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7913 ?
Unique (%)100.0%

Sample

1st row000000000FD66B8C
2nd row000000000FD66B8D
3rd row000000000FD66B8E
4th row000000000FD66B8F
5th row000000000FD66B90
ValueCountFrequency (%)
000000000fd66b8c 1
 
< 0.1%
000000000fd66b8e 1
 
< 0.1%
000000000fd66b90 1
 
< 0.1%
000000000fd66b91 1
 
< 0.1%
000000000fd66b92 1
 
< 0.1%
000000000fd66b93 1
 
< 0.1%
000000000fd66b94 1
 
< 0.1%
000000000fd66b95 1
 
< 0.1%
000000000fd66b96 1
 
< 0.1%
000000000fd66b97 1
 
< 0.1%
Other values (7903) 7903
99.9%
2023-10-11T09:49:41.044961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 72774
57.5%
6 10087
 
8.0%
F 9027
 
7.1%
D 9014
 
7.1%
7 5598
 
4.4%
8 3837
 
3.0%
1 2189
 
1.7%
C 1738
 
1.4%
E 1633
 
1.3%
9 1618
 
1.3%
Other values (6) 9093
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102233
80.7%
Uppercase Letter 24375
 
19.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72774
71.2%
6 10087
 
9.9%
7 5598
 
5.5%
8 3837
 
3.8%
1 2189
 
2.1%
9 1618
 
1.6%
4 1597
 
1.6%
2 1574
 
1.5%
3 1566
 
1.5%
5 1393
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
F 9027
37.0%
D 9014
37.0%
C 1738
 
7.1%
E 1633
 
6.7%
B 1609
 
6.6%
A 1354
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 102233
80.7%
Latin 24375
 
19.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72774
71.2%
6 10087
 
9.9%
7 5598
 
5.5%
8 3837
 
3.8%
1 2189
 
2.1%
9 1618
 
1.6%
4 1597
 
1.6%
2 1574
 
1.5%
3 1566
 
1.5%
5 1393
 
1.4%
Latin
ValueCountFrequency (%)
F 9027
37.0%
D 9014
37.0%
C 1738
 
7.1%
E 1633
 
6.7%
B 1609
 
6.6%
A 1354
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72774
57.5%
6 10087
 
8.0%
F 9027
 
7.1%
D 9014
 
7.1%
7 5598
 
4.4%
8 3837
 
3.0%
1 2189
 
1.7%
C 1738
 
1.4%
E 1633
 
1.3%
9 1618
 
1.3%
Other values (6) 9093
 
7.2%

No_
Text

UNIQUE 

Distinct7913
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
2023-10-11T09:49:41.345428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10.152028
Min length8

Characters and Unicode

Total characters80333
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7913 ?
Unique (%)100.0%

Sample

1st rowBRPR-00003
2nd rowBRPR-00004
3rd rowBRPR-00005
4th rowBRPR-00006
5th rowBRPR-00007
ValueCountFrequency (%)
brpr-00003 1
 
< 0.1%
brpr-00005 1
 
< 0.1%
brpr-00007 1
 
< 0.1%
brpr-00008 1
 
< 0.1%
brpr-00009 1
 
< 0.1%
brpr-00010 1
 
< 0.1%
brpr-00011 1
 
< 0.1%
brpr-00012 1
 
< 0.1%
brpr-00013 1
 
< 0.1%
brpr-00014 1
 
< 0.1%
Other values (7903) 7903
99.9%
2023-10-11T09:49:41.770786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16998
21.2%
P 9237
11.5%
R 8745
10.9%
- 6416
 
8.0%
1 3977
 
5.0%
E 3791
 
4.7%
2 2804
 
3.5%
M 2767
 
3.4%
3 2553
 
3.2%
C 2427
 
3.0%
Other values (20) 20618
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37833
47.1%
Uppercase Letter 36084
44.9%
Dash Punctuation 6416
 
8.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 9237
25.6%
R 8745
24.2%
E 3791
10.5%
M 2767
 
7.7%
C 2427
 
6.7%
S 2074
 
5.7%
L 1754
 
4.9%
G 1251
 
3.5%
N 846
 
2.3%
O 605
 
1.7%
Other values (9) 2587
 
7.2%
Decimal Number
ValueCountFrequency (%)
0 16998
44.9%
1 3977
 
10.5%
2 2804
 
7.4%
3 2553
 
6.7%
4 2338
 
6.2%
5 2113
 
5.6%
6 1952
 
5.2%
7 1779
 
4.7%
8 1698
 
4.5%
9 1621
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 6416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44249
55.1%
Latin 36084
44.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 9237
25.6%
R 8745
24.2%
E 3791
10.5%
M 2767
 
7.7%
C 2427
 
6.7%
S 2074
 
5.7%
L 1754
 
4.9%
G 1251
 
3.5%
N 846
 
2.3%
O 605
 
1.7%
Other values (9) 2587
 
7.2%
Common
ValueCountFrequency (%)
0 16998
38.4%
- 6416
 
14.5%
1 3977
 
9.0%
2 2804
 
6.3%
3 2553
 
5.8%
4 2338
 
5.3%
5 2113
 
4.8%
6 1952
 
4.4%
7 1779
 
4.0%
8 1698
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16998
21.2%
P 9237
11.5%
R 8745
10.9%
- 6416
 
8.0%
1 3977
 
5.0%
E 3791
 
4.7%
2 2804
 
3.5%
M 2767
 
3.4%
3 2553
 
3.2%
C 2427
 
3.0%
Other values (20) 20618
25.7%

PR Date
Date

Distinct1898
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
Minimum2013-01-02 00:00:00
Maximum2021-07-11 00:00:00
2023-10-11T09:49:41.954186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-11T09:49:42.105822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Printing Date
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
-53688
7913 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters47478
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-53688
2nd row-53688
3rd row-53688
4th row-53688
5th row-53688

Common Values

ValueCountFrequency (%)
-53688 7913
100.0%

Length

2023-10-11T09:49:42.228462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:42.347298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
53688 7913
100.0%

Most occurring characters

ValueCountFrequency (%)
8 15826
33.3%
- 7913
16.7%
5 7913
16.7%
3 7913
16.7%
6 7913
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39565
83.3%
Dash Punctuation 7913
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 15826
40.0%
5 7913
20.0%
3 7913
20.0%
6 7913
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 7913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47478
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 15826
33.3%
- 7913
16.7%
5 7913
16.7%
3 7913
16.7%
6 7913
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 15826
33.3%
- 7913
16.7%
5 7913
16.7%
3 7913
16.7%
6 7913
16.7%

Department
Real number (ℝ)

MISSING 

Distinct52
Distinct (%)0.7%
Missing252
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean690.14463
Minimum100
Maximum1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-10-11T09:49:42.450211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile120
Q1420
median833
Q3834
95-th percentile843
Maximum1010
Range910
Interquartile range (IQR)414

Descriptive statistics

Standard deviation258.05338
Coefficient of variation (CV)0.37391204
Kurtosis0.037206738
Mean690.14463
Median Absolute Deviation (MAD)2
Skewness-1.2730845
Sum5287198
Variance66591.548
MonotonicityNot monotonic
2023-10-11T09:49:42.603834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
833 2472
31.2%
834 935
 
11.8%
420 527
 
6.7%
835 519
 
6.6%
100 369
 
4.7%
843 350
 
4.4%
822 336
 
4.2%
330 279
 
3.5%
813 243
 
3.1%
1010 188
 
2.4%
Other values (42) 1443
18.2%
(Missing) 252
 
3.2%
ValueCountFrequency (%)
100 369
4.7%
110 1
 
< 0.1%
120 146
 
1.8%
131 2
 
< 0.1%
132 1
 
< 0.1%
140 102
 
1.3%
150 49
 
0.6%
151 4
 
0.1%
152 2
 
< 0.1%
160 1
 
< 0.1%
ValueCountFrequency (%)
1010 188
2.4%
1000 5
 
0.1%
905 3
 
< 0.1%
848 10
 
0.1%
847 92
 
1.2%
846 4
 
0.1%
845 13
 
0.2%
844 3
 
< 0.1%
843 350
4.4%
842 10
 
0.1%

Intended For
Text

MISSING 

Distinct4264
Distinct (%)55.8%
Missing268
Missing (%)3.4%
Memory size61.9 KiB
2023-10-11T09:49:42.899660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length85
Median length54
Mean length21.297842
Min length2

Characters and Unicode

Total characters162822
Distinct characters75
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3347 ?
Unique (%)43.8%

Sample

1st row2X150MW POWER PLANT
2nd row2X150MW POWER PLANT
3rd row2X150MW POWER PLANT
4th row2X150MW POWER PLANT
5th row2X150MW POWER PLANT
ValueCountFrequency (%)
plant 659
 
2.7%
slpgc 632
 
2.6%
power 559
 
2.3%
unit 523
 
2.1%
maintenance 447
 
1.8%
419
 
1.7%
mechanical 340
 
1.4%
boiler 308
 
1.2%
mw 303
 
1.2%
system 302
 
1.2%
Other values (2366) 20191
81.8%
2023-10-11T09:49:43.386217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17068
 
10.5%
e 10773
 
6.6%
n 8081
 
5.0%
a 7751
 
4.8%
i 7203
 
4.4%
t 6961
 
4.3%
r 6691
 
4.1%
o 5945
 
3.7%
l 4901
 
3.0%
E 4678
 
2.9%
Other values (65) 82770
50.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80955
49.7%
Uppercase Letter 58577
36.0%
Space Separator 17068
 
10.5%
Decimal Number 4356
 
2.7%
Other Punctuation 1140
 
0.7%
Dash Punctuation 576
 
0.4%
Open Punctuation 76
 
< 0.1%
Close Punctuation 73
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10773
13.3%
n 8081
10.0%
a 7751
9.6%
i 7203
8.9%
t 6961
8.6%
r 6691
8.3%
o 5945
 
7.3%
l 4901
 
6.1%
s 3480
 
4.3%
c 3380
 
4.2%
Other values (16) 15789
19.5%
Uppercase Letter
ValueCountFrequency (%)
E 4678
 
8.0%
S 4544
 
7.8%
P 4465
 
7.6%
A 4218
 
7.2%
C 4133
 
7.1%
T 3968
 
6.8%
R 3605
 
6.2%
M 3486
 
6.0%
O 3225
 
5.5%
L 3184
 
5.4%
Other values (16) 19071
32.6%
Decimal Number
ValueCountFrequency (%)
2 949
21.8%
1 731
16.8%
5 697
16.0%
0 627
14.4%
3 599
13.8%
4 548
12.6%
6 103
 
2.4%
9 45
 
1.0%
8 30
 
0.7%
7 27
 
0.6%
Other Punctuation
ValueCountFrequency (%)
& 428
37.5%
. 323
28.3%
/ 260
22.8%
, 46
 
4.0%
# 43
 
3.8%
' 37
 
3.2%
% 2
 
0.2%
: 1
 
0.1%
Space Separator
ValueCountFrequency (%)
17068
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 576
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 139532
85.7%
Common 23290
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10773
 
7.7%
n 8081
 
5.8%
a 7751
 
5.6%
i 7203
 
5.2%
t 6961
 
5.0%
r 6691
 
4.8%
o 5945
 
4.3%
l 4901
 
3.5%
E 4678
 
3.4%
S 4544
 
3.3%
Other values (42) 72004
51.6%
Common
ValueCountFrequency (%)
17068
73.3%
2 949
 
4.1%
1 731
 
3.1%
5 697
 
3.0%
0 627
 
2.7%
3 599
 
2.6%
- 576
 
2.5%
4 548
 
2.4%
& 428
 
1.8%
. 323
 
1.4%
Other values (13) 744
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17068
 
10.5%
e 10773
 
6.6%
n 8081
 
5.0%
a 7751
 
4.8%
i 7203
 
4.4%
t 6961
 
4.3%
r 6691
 
4.1%
o 5945
 
3.7%
l 4901
 
3.0%
E 4678
 
2.9%
Other values (65) 82770
50.8%

Phase of Work
Text

MISSING 

Distinct1376
Distinct (%)45.8%
Missing4907
Missing (%)62.0%
Memory size61.9 KiB
2023-10-11T09:49:43.671426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length120
Median length47
Mean length20.337991
Min length1

Characters and Unicode

Total characters61136
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1087 ?
Unique (%)36.2%

Sample

1st rowRegeneration for resin of mixed bed exchangers
2nd rowRegeneration for resin of mixed bed exchangers
3rd rowTransferring Liquid Chemicals
4th rowRegeneration for resin of mixed bed exchangers
5th rowRegeneration for resin of mixed bed exchangers
ValueCountFrequency (%)
of 505
 
5.9%
maintenance 331
 
3.9%
spare 313
 
3.7%
274
 
3.2%
for 179
 
2.1%
supply 173
 
2.0%
analysis 156
 
1.8%
calibration 151
 
1.8%
parts 138
 
1.6%
and 137
 
1.6%
Other values (1337) 6165
72.3%
2023-10-11T09:49:44.118622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5536
 
9.1%
n 3666
 
6.0%
e 3512
 
5.7%
a 3178
 
5.2%
i 2815
 
4.6%
t 2484
 
4.1%
o 2352
 
3.8%
A 2232
 
3.7%
S 2137
 
3.5%
r 2105
 
3.4%
Other values (65) 31119
50.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30834
50.4%
Uppercase Letter 21142
34.6%
Space Separator 5536
 
9.1%
Decimal Number 2169
 
3.5%
Other Punctuation 631
 
1.0%
Dash Punctuation 601
 
1.0%
Close Punctuation 112
 
0.2%
Open Punctuation 111
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3666
11.9%
e 3512
11.4%
a 3178
10.3%
i 2815
9.1%
t 2484
 
8.1%
o 2352
 
7.6%
r 2105
 
6.8%
l 1618
 
5.2%
s 1365
 
4.4%
c 1215
 
3.9%
Other values (16) 6524
21.2%
Uppercase Letter
ValueCountFrequency (%)
A 2232
 
10.6%
S 2137
 
10.1%
E 1717
 
8.1%
R 1534
 
7.3%
P 1484
 
7.0%
N 1312
 
6.2%
T 1296
 
6.1%
C 1269
 
6.0%
O 1251
 
5.9%
M 1236
 
5.8%
Other values (16) 5674
26.8%
Decimal Number
ValueCountFrequency (%)
0 658
30.3%
2 332
15.3%
3 279
12.9%
1 275
12.7%
4 253
 
11.7%
5 178
 
8.2%
6 68
 
3.1%
9 65
 
3.0%
8 35
 
1.6%
7 26
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/ 241
38.2%
, 189
30.0%
& 114
18.1%
. 69
 
10.9%
' 10
 
1.6%
# 3
 
0.5%
; 2
 
0.3%
@ 2
 
0.3%
: 1
 
0.2%
Space Separator
ValueCountFrequency (%)
5536
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 601
100.0%
Close Punctuation
ValueCountFrequency (%)
) 112
100.0%
Open Punctuation
ValueCountFrequency (%)
( 111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51976
85.0%
Common 9160
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3666
 
7.1%
e 3512
 
6.8%
a 3178
 
6.1%
i 2815
 
5.4%
t 2484
 
4.8%
o 2352
 
4.5%
A 2232
 
4.3%
S 2137
 
4.1%
r 2105
 
4.0%
E 1717
 
3.3%
Other values (42) 25778
49.6%
Common
ValueCountFrequency (%)
5536
60.4%
0 658
 
7.2%
- 601
 
6.6%
2 332
 
3.6%
3 279
 
3.0%
1 275
 
3.0%
4 253
 
2.8%
/ 241
 
2.6%
, 189
 
2.1%
5 178
 
1.9%
Other values (13) 618
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5536
 
9.1%
n 3666
 
6.0%
e 3512
 
5.7%
a 3178
 
5.2%
i 2815
 
4.6%
t 2484
 
4.1%
o 2352
 
3.8%
A 2232
 
3.7%
S 2137
 
3.5%
r 2105
 
3.4%
Other values (65) 31119
50.9%

Reference
Text

MISSING 

Distinct1369
Distinct (%)62.4%
Missing5720
Missing (%)72.3%
Memory size61.9 KiB
2023-10-11T09:49:44.377530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length50
Median length45
Mean length18.008664
Min length1

Characters and Unicode

Total characters39493
Distinct characters71
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1287 ?
Unique (%)58.7%

Sample

1st rowBILLING INVOICE
2nd rowATTACHED BROKER SOA
3rd rowATTACHED BROKER SOA
4th rowATTACHED BILLING STATEMENT
5th rowATTACHED BROKER'S BILLING
ValueCountFrequency (%)
attached 462
 
10.7%
form 254
 
5.9%
request 252
 
5.9%
payment 251
 
5.8%
recommended 231
 
5.4%
mainpac 226
 
5.3%
billing 197
 
4.6%
item 193
 
4.5%
invoice 182
 
4.2%
kms 70
 
1.6%
Other values (1448) 1984
46.1%
2023-10-11T09:49:45.054695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3093
 
7.8%
0 2752
 
7.0%
e 2392
 
6.1%
2113
 
5.4%
1 1630
 
4.1%
t 1570
 
4.0%
M 1515
 
3.8%
2 1330
 
3.4%
m 1289
 
3.3%
P 1215
 
3.1%
Other values (61) 20594
52.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12969
32.8%
Uppercase Letter 12336
31.2%
Decimal Number 8778
22.2%
Dash Punctuation 3093
 
7.8%
Space Separator 2113
 
5.4%
Other Punctuation 191
 
0.5%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2392
18.4%
t 1570
12.1%
m 1289
9.9%
a 1135
8.8%
n 1028
7.9%
c 882
 
6.8%
d 837
 
6.5%
o 616
 
4.7%
i 533
 
4.1%
h 404
 
3.1%
Other values (15) 2283
17.6%
Uppercase Letter
ValueCountFrequency (%)
M 1515
12.3%
P 1215
9.8%
L 1089
8.8%
I 1040
8.4%
D 1009
8.2%
S 998
8.1%
E 938
 
7.6%
C 766
 
6.2%
R 727
 
5.9%
A 661
 
5.4%
Other values (14) 2378
19.3%
Decimal Number
ValueCountFrequency (%)
0 2752
31.4%
1 1630
18.6%
2 1330
15.2%
8 572
 
6.5%
9 568
 
6.5%
5 425
 
4.8%
3 421
 
4.8%
4 390
 
4.4%
7 368
 
4.2%
6 322
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 121
63.4%
/ 22
 
11.5%
. 21
 
11.0%
: 10
 
5.2%
' 9
 
4.7%
# 6
 
3.1%
& 2
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 3093
100.0%
Space Separator
ValueCountFrequency (%)
2113
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Math Symbol
ValueCountFrequency (%)
| 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25305
64.1%
Common 14188
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2392
 
9.5%
t 1570
 
6.2%
M 1515
 
6.0%
m 1289
 
5.1%
P 1215
 
4.8%
a 1135
 
4.5%
L 1089
 
4.3%
I 1040
 
4.1%
n 1028
 
4.1%
D 1009
 
4.0%
Other values (39) 12023
47.5%
Common
ValueCountFrequency (%)
- 3093
21.8%
0 2752
19.4%
2113
14.9%
1 1630
11.5%
2 1330
9.4%
8 572
 
4.0%
9 568
 
4.0%
5 425
 
3.0%
3 421
 
3.0%
4 390
 
2.7%
Other values (12) 894
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3093
 
7.8%
0 2752
 
7.0%
e 2392
 
6.1%
2113
 
5.4%
1 1630
 
4.1%
t 1570
 
4.0%
M 1515
 
3.8%
2 1330
 
3.4%
m 1289
 
3.3%
P 1215
 
3.1%
Other values (61) 20594
52.1%

Work Description
Text

MISSING 

Distinct1831
Distinct (%)78.1%
Missing5568
Missing (%)70.4%
Memory size61.9 KiB
2023-10-11T09:49:45.504466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length124
Median length45
Mean length28.856716
Min length1

Characters and Unicode

Total characters67669
Distinct characters74
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1587 ?
Unique (%)67.7%

Sample

1st rowATTACHED BILLING INVOICE
2nd rowATTACHED BILLING INVOICE
3rd rowATTACHED BILLING INVOICE
4th rowATTACHED BILLING INVOICE
5th rowATTACHED BILLING INVOICE
ValueCountFrequency (%)
of 1017
 
10.1%
for 259
 
2.6%
and 246
 
2.5%
purchase 233
 
2.3%
calibration 185
 
1.8%
supply 176
 
1.8%
162
 
1.6%
water 162
 
1.6%
replacement 158
 
1.6%
installation 141
 
1.4%
Other values (1775) 7282
72.7%
2023-10-11T09:49:46.720753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7698
 
11.4%
e 4440
 
6.6%
a 3633
 
5.4%
o 3320
 
4.9%
n 3290
 
4.9%
i 3280
 
4.8%
r 2917
 
4.3%
t 2849
 
4.2%
l 2342
 
3.5%
s 1999
 
3.0%
Other values (64) 31901
47.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38290
56.6%
Uppercase Letter 19073
28.2%
Space Separator 7698
 
11.4%
Decimal Number 1657
 
2.4%
Other Punctuation 527
 
0.8%
Dash Punctuation 233
 
0.3%
Open Punctuation 96
 
0.1%
Close Punctuation 95
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4440
11.6%
a 3633
9.5%
o 3320
 
8.7%
n 3290
 
8.6%
i 3280
 
8.6%
r 2917
 
7.6%
t 2849
 
7.4%
l 2342
 
6.1%
s 1999
 
5.2%
c 1500
 
3.9%
Other values (16) 8720
22.8%
Uppercase Letter
ValueCountFrequency (%)
A 1562
 
8.2%
T 1549
 
8.1%
R 1511
 
7.9%
E 1481
 
7.8%
I 1411
 
7.4%
S 1309
 
6.9%
C 1166
 
6.1%
P 1158
 
6.1%
O 1072
 
5.6%
N 971
 
5.1%
Other values (16) 5883
30.8%
Decimal Number
ValueCountFrequency (%)
0 547
33.0%
3 215
 
13.0%
2 198
 
11.9%
5 186
 
11.2%
1 159
 
9.6%
4 157
 
9.5%
6 70
 
4.2%
9 55
 
3.3%
8 43
 
2.6%
7 27
 
1.6%
Other Punctuation
ValueCountFrequency (%)
, 223
42.3%
& 137
26.0%
/ 87
 
16.5%
. 68
 
12.9%
# 6
 
1.1%
' 3
 
0.6%
; 2
 
0.4%
: 1
 
0.2%
Space Separator
ValueCountFrequency (%)
7698
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 233
100.0%
Open Punctuation
ValueCountFrequency (%)
( 96
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57363
84.8%
Common 10306
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4440
 
7.7%
a 3633
 
6.3%
o 3320
 
5.8%
n 3290
 
5.7%
i 3280
 
5.7%
r 2917
 
5.1%
t 2849
 
5.0%
l 2342
 
4.1%
s 1999
 
3.5%
A 1562
 
2.7%
Other values (42) 27731
48.3%
Common
ValueCountFrequency (%)
7698
74.7%
0 547
 
5.3%
- 233
 
2.3%
, 223
 
2.2%
3 215
 
2.1%
2 198
 
1.9%
5 186
 
1.8%
1 159
 
1.5%
4 157
 
1.5%
& 137
 
1.3%
Other values (12) 553
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7698
 
11.4%
e 4440
 
6.6%
a 3633
 
5.4%
o 3320
 
4.9%
n 3290
 
4.9%
i 3280
 
4.8%
r 2917
 
4.3%
t 2849
 
4.2%
l 2342
 
3.5%
s 1999
 
3.0%
Other values (64) 31901
47.1%

Date Needed
Date

Distinct1974
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
Minimum1753-01-01 00:00:00
Maximum2025-11-23 00:00:00
2023-10-11T09:49:47.138956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-11T09:49:47.438296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Batch
Text

MISSING 

Distinct970
Distinct (%)100.0%
Missing6943
Missing (%)87.7%
Memory size61.9 KiB
2023-10-11T09:49:48.021179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10.010309
Min length8

Characters and Unicode

Total characters9710
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique970 ?
Unique (%)100.0%

Sample

1st rowBRPR-00002
2nd rowCHEPR-0040
3rd rowCHEPR-0315
4th rowCRPR-0004
5th rowELEPR-0041
ValueCountFrequency (%)
smppr00637 1
 
0.1%
smppr00029 1
 
0.1%
slpr-0000000366 1
 
0.1%
incpr-0100 1
 
0.1%
chepr-0040 1
 
0.1%
chepr-0315 1
 
0.1%
crpr-0004 1
 
0.1%
elepr-0041 1
 
0.1%
elepr-0054 1
 
0.1%
elepr-0410 1
 
0.1%
Other values (960) 960
99.0%
2023-10-11T09:49:48.585701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2245
23.1%
P 1913
19.7%
R 974
10.0%
S 945
9.7%
M 945
9.7%
1 309
 
3.2%
3 303
 
3.1%
2 302
 
3.1%
4 294
 
3.0%
5 293
 
3.0%
Other values (17) 1187
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4841
49.9%
Uppercase Letter 4840
49.8%
Dash Punctuation 29
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1913
39.5%
R 974
20.1%
S 945
19.5%
M 945
19.5%
E 17
 
0.4%
C 11
 
0.2%
L 8
 
0.2%
N 7
 
0.1%
G 5
 
0.1%
I 3
 
0.1%
Other values (6) 12
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 2245
46.4%
1 309
 
6.4%
3 303
 
6.3%
2 302
 
6.2%
4 294
 
6.1%
5 293
 
6.1%
6 291
 
6.0%
8 288
 
5.9%
7 286
 
5.9%
9 230
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4870
50.2%
Latin 4840
49.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 1913
39.5%
R 974
20.1%
S 945
19.5%
M 945
19.5%
E 17
 
0.4%
C 11
 
0.2%
L 8
 
0.2%
N 7
 
0.1%
G 5
 
0.1%
I 3
 
0.1%
Other values (6) 12
 
0.2%
Common
ValueCountFrequency (%)
0 2245
46.1%
1 309
 
6.3%
3 303
 
6.2%
2 302
 
6.2%
4 294
 
6.0%
5 293
 
6.0%
6 291
 
6.0%
8 288
 
5.9%
7 286
 
5.9%
9 230
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2245
23.1%
P 1913
19.7%
R 974
10.0%
S 945
9.7%
M 945
9.7%
1 309
 
3.2%
3 303
 
3.1%
2 302
 
3.1%
4 294
 
3.0%
5 293
 
3.0%
Other values (17) 1187
12.2%

Template
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
REQ.
7913 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters31652
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREQ.
2nd rowREQ.
3rd rowREQ.
4th rowREQ.
5th rowREQ.

Common Values

ValueCountFrequency (%)
REQ. 7913
100.0%

Length

2023-10-11T09:49:48.806798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:48.954384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
req 7913
100.0%

Most occurring characters

ValueCountFrequency (%)
R 7913
25.0%
E 7913
25.0%
Q 7913
25.0%
. 7913
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23739
75.0%
Other Punctuation 7913
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 7913
33.3%
E 7913
33.3%
Q 7913
33.3%
Other Punctuation
ValueCountFrequency (%)
. 7913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23739
75.0%
Common 7913
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 7913
33.3%
E 7913
33.3%
Q 7913
33.3%
Common
ValueCountFrequency (%)
. 7913
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 7913
25.0%
E 7913
25.0%
Q 7913
25.0%
. 7913
25.0%

External PR No_
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7913
Missing (%)100.0%
Memory size61.9 KiB

Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
1
6827 
0
1086 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7913
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6827
86.3%
0 1086
 
13.7%

Length

2023-10-11T09:49:49.085652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:49.225352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 6827
86.3%
0 1086
 
13.7%

Most occurring characters

ValueCountFrequency (%)
1 6827
86.3%
0 1086
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6827
86.3%
0 1086
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Common 7913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6827
86.3%
0 1086
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6827
86.3%
0 1086
 
13.7%

ReOpened
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
1
7124 
0
789 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7913
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7124
90.0%
0 789
 
10.0%

Length

2023-10-11T09:49:49.389079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:49.584450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 7124
90.0%
0 789
 
10.0%

Most occurring characters

ValueCountFrequency (%)
1 7124
90.0%
0 789
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7124
90.0%
0 789
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7124
90.0%
0 789
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7124
90.0%
0 789
 
10.0%

Release Date
Date

Distinct1218
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
Minimum1753-01-01 00:00:00
Maximum2021-07-12 00:00:00
2023-10-11T09:49:49.704994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-11T09:49:50.056723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Remarks
Text

MISSING 

Distinct6762
Distinct (%)98.7%
Missing1065
Missing (%)13.5%
Memory size61.9 KiB
2023-10-11T09:49:50.461098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length150
Median length129
Mean length49.566297
Min length3

Characters and Unicode

Total characters339430
Distinct characters83
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6730 ?
Unique (%)98.3%

Sample

1st rowSLBPO-000028 OCEAN STAR FREIGHT EXPRESS
2nd rowSLBPO-000002 AMERICAN FREIGHT FORWARDERS, INC.
3rd rowSLBPO-000003 CARGO PLUS FREIGHT EXPRESS
4th rowSLBPO-000020 GCLPI-GATEWAY CONTAINER LINE PHILS. INC.
5th rowSLBPO-000019 CARGO PLUS FREIGHT EXPRESS
ValueCountFrequency (%)
2130
 
5.2%
inc 1782
 
4.3%
services 603
 
1.5%
industrial 543
 
1.3%
for 536
 
1.3%
ltd 497
 
1.2%
te 476
 
1.2%
rcvd 462
 
1.1%
ind'l 446
 
1.1%
corporation 432
 
1.1%
Other values (11525) 33132
80.7%
2023-10-11T09:49:50.996722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34300
 
10.1%
0 27036
 
8.0%
S 17618
 
5.2%
O 16604
 
4.9%
L 14511
 
4.3%
E 13838
 
4.1%
- 12279
 
3.6%
R 11539
 
3.4%
P 11376
 
3.4%
I 10939
 
3.2%
Other values (73) 169390
49.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 166161
49.0%
Decimal Number 64155
 
18.9%
Lowercase Letter 49170
 
14.5%
Space Separator 34300
 
10.1%
Dash Punctuation 12281
 
3.6%
Other Punctuation 12173
 
3.6%
Open Punctuation 594
 
0.2%
Close Punctuation 593
 
0.2%
Math Symbol 2
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 17618
10.6%
O 16604
10.0%
L 14511
 
8.7%
E 13838
 
8.3%
R 11539
 
6.9%
P 11376
 
6.8%
I 10939
 
6.6%
N 10713
 
6.4%
T 10571
 
6.4%
C 9128
 
5.5%
Other values (16) 39324
23.7%
Lowercase Letter
ValueCountFrequency (%)
e 5493
11.2%
r 4699
9.6%
i 4299
 
8.7%
n 4248
 
8.6%
a 3818
 
7.8%
o 3756
 
7.6%
s 3310
 
6.7%
t 3144
 
6.4%
c 2879
 
5.9%
l 2521
 
5.1%
Other values (16) 11003
22.4%
Other Punctuation
ValueCountFrequency (%)
/ 4775
39.2%
. 3553
29.2%
, 1940
15.9%
& 578
 
4.7%
' 573
 
4.7%
: 393
 
3.2%
; 347
 
2.9%
# 6
 
< 0.1%
" 4
 
< 0.1%
% 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 27036
42.1%
1 7719
 
12.0%
2 6090
 
9.5%
3 4166
 
6.5%
4 3620
 
5.6%
7 3353
 
5.2%
9 3270
 
5.1%
8 3123
 
4.9%
5 2942
 
4.6%
6 2836
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 12279
> 99.9%
– 2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
| 1
50.0%
+ 1
50.0%
Space Separator
ValueCountFrequency (%)
34300
100.0%
Open Punctuation
ValueCountFrequency (%)
( 594
100.0%
Close Punctuation
ValueCountFrequency (%)
) 593
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 215331
63.4%
Common 124099
36.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 17618
 
8.2%
O 16604
 
7.7%
L 14511
 
6.7%
E 13838
 
6.4%
R 11539
 
5.4%
P 11376
 
5.3%
I 10939
 
5.1%
N 10713
 
5.0%
T 10571
 
4.9%
C 9128
 
4.2%
Other values (42) 88494
41.1%
Common
ValueCountFrequency (%)
34300
27.6%
0 27036
21.8%
- 12279
 
9.9%
1 7719
 
6.2%
2 6090
 
4.9%
/ 4775
 
3.8%
3 4166
 
3.4%
4 3620
 
2.9%
. 3553
 
2.9%
7 3353
 
2.7%
Other values (21) 17208
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 339428
> 99.9%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34300
 
10.1%
0 27036
 
8.0%
S 17618
 
5.2%
O 16604
 
4.9%
L 14511
 
4.3%
E 13838
 
4.1%
- 12279
 
3.6%
R 11539
 
3.4%
P 11376
 
3.4%
I 10939
 
3.2%
Other values (72) 169388
49.9%
Punctuation
ValueCountFrequency (%)
– 2
100.0%

Dimension Set ID
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3637.6783
Minimum0
Maximum86069
Zeros2691
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-10-11T09:49:51.172027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3449
Q34379
95-th percentile6342
Maximum86069
Range86069
Interquartile range (IQR)4379

Descriptive statistics

Standard deviation5509.0804
Coefficient of variation (CV)1.5144496
Kurtosis20.138097
Mean3637.6783
Median Absolute Deviation (MAD)2028
Skewness3.8611153
Sum28784948
Variance30349967
MonotonicityNot monotonic
2023-10-11T09:49:51.287488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 2691
34.0%
3449 1837
23.2%
4379 735
 
9.3%
5477 416
 
5.3%
5097 362
 
4.6%
871 278
 
3.5%
3533 269
 
3.4%
26977 188
 
2.4%
5476 182
 
2.3%
3539 174
 
2.2%
Other values (27) 781
 
9.9%
ValueCountFrequency (%)
0 2691
34.0%
680 29
 
0.4%
871 278
 
3.5%
1127 3
 
< 0.1%
1481 1
 
< 0.1%
2031 5
 
0.1%
2141 43
 
0.5%
3196 74
 
0.9%
3402 1
 
< 0.1%
3449 1837
23.2%
ValueCountFrequency (%)
86069 1
 
< 0.1%
43930 10
 
0.1%
31312 47
 
0.6%
30008 11
 
0.1%
29232 2
 
< 0.1%
26977 188
2.4%
24573 2
 
< 0.1%
22634 2
 
< 0.1%
18102 121
1.5%
10677 1
 
< 0.1%

Doc_ No_ Occurrence
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
1
6973 
0
940 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7913
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6973
88.1%
0 940
 
11.9%

Length

2023-10-11T09:49:51.404073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:51.760412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 6973
88.1%
0 940
 
11.9%

Most occurring characters

ValueCountFrequency (%)
1 6973
88.1%
0 940
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6973
88.1%
0 940
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 7913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6973
88.1%
0 940
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6973
88.1%
0 940
 
11.9%

Printed
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
0
7913 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7913
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7913
100.0%

Length

2023-10-11T09:49:51.928733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:52.113189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7913
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7913
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7913
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7913
100.0%

Date PR Received
Date

Distinct1019
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
Minimum1753-01-01 00:00:00
Maximum2021-07-09 00:00:00
2023-10-11T09:49:52.252462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-11T09:49:52.416651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PR Monitoring Status
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.353848
Minimum0
Maximum19
Zeros126
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-10-11T09:49:52.545174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q110
median10
Q310
95-th percentile17
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.83613
Coefficient of variation (CV)0.27392038
Kurtosis4.2098315
Mean10.353848
Median Absolute Deviation (MAD)0
Skewness-0.012981846
Sum81930
Variance8.0436331
MonotonicityNot monotonic
2023-10-11T09:49:52.678021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10 5757
72.8%
17 765
 
9.7%
11 633
 
8.0%
8 313
 
4.0%
0 126
 
1.6%
3 87
 
1.1%
4 80
 
1.0%
2 61
 
0.8%
14 44
 
0.6%
9 23
 
0.3%
Other values (4) 24
 
0.3%
ValueCountFrequency (%)
0 126
 
1.6%
2 61
 
0.8%
3 87
 
1.1%
4 80
 
1.0%
5 1
 
< 0.1%
8 313
 
4.0%
9 23
 
0.3%
10 5757
72.8%
11 633
 
8.0%
13 10
 
0.1%
ValueCountFrequency (%)
19 8
 
0.1%
17 765
 
9.7%
15 5
 
0.1%
14 44
 
0.6%
13 10
 
0.1%
11 633
 
8.0%
10 5757
72.8%
9 23
 
0.3%
8 313
 
4.0%
5 1
 
< 0.1%

Purchaser Code
Categorical

MISSING 

Distinct16
Distinct (%)0.2%
Missing903
Missing (%)11.4%
Memory size61.9 KiB
JCC
2783 
ABM
815 
AFR
775 
RSA
622 
LTL
534 
Other values (11)
1481 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21030
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHSM
2nd rowHSM
3rd rowHSM
4th rowHSM
5th rowHSM

Common Values

ValueCountFrequency (%)
JCC 2783
35.2%
ABM 815
 
10.3%
AFR 775
 
9.8%
RSA 622
 
7.9%
LTL 534
 
6.7%
PBB 443
 
5.6%
MMM 337
 
4.3%
JUB 262
 
3.3%
HSM 124
 
1.6%
IGM 117
 
1.5%
Other values (6) 198
 
2.5%
(Missing) 903
 
11.4%

Length

2023-10-11T09:49:52.821589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jcc 2783
39.7%
abm 815
 
11.6%
afr 775
 
11.1%
rsa 622
 
8.9%
ltl 534
 
7.6%
pbb 443
 
6.3%
mmm 337
 
4.8%
jub 262
 
3.7%
hsm 124
 
1.8%
igm 117
 
1.7%
Other values (6) 198
 
2.8%

Most occurring characters

ValueCountFrequency (%)
C 5586
26.6%
J 3080
14.6%
A 2308
11.0%
M 2290
10.9%
B 2054
 
9.8%
R 1397
 
6.6%
L 1068
 
5.1%
F 775
 
3.7%
S 763
 
3.6%
T 539
 
2.6%
Other values (8) 1170
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21030
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 5586
26.6%
J 3080
14.6%
A 2308
11.0%
M 2290
10.9%
B 2054
 
9.8%
R 1397
 
6.6%
L 1068
 
5.1%
F 775
 
3.7%
S 763
 
3.6%
T 539
 
2.6%
Other values (8) 1170
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 21030
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 5586
26.6%
J 3080
14.6%
A 2308
11.0%
M 2290
10.9%
B 2054
 
9.8%
R 1397
 
6.6%
L 1068
 
5.1%
F 775
 
3.7%
S 763
 
3.6%
T 539
 
2.6%
Other values (8) 1170
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 5586
26.6%
J 3080
14.6%
A 2308
11.0%
M 2290
10.9%
B 2054
 
9.8%
R 1397
 
6.6%
L 1068
 
5.1%
F 775
 
3.7%
S 763
 
3.6%
T 539
 
2.6%
Other values (8) 1170
 
5.6%

Location Code
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)3.6%
Missing7858
Missing (%)99.3%
Memory size61.9 KiB
HEAD OFC
49 
CALACA
6 

Length

Max length8
Median length8
Mean length7.7818182
Min length6

Characters and Unicode

Total characters428
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHEAD OFC
2nd rowHEAD OFC
3rd rowHEAD OFC
4th rowHEAD OFC
5th rowHEAD OFC

Common Values

ValueCountFrequency (%)
HEAD OFC 49
 
0.6%
CALACA 6
 
0.1%
(Missing) 7858
99.3%

Length

2023-10-11T09:49:53.014781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:53.154357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
head 49
47.1%
ofc 49
47.1%
calaca 6
 
5.8%

Most occurring characters

ValueCountFrequency (%)
A 67
15.7%
C 61
14.3%
H 49
11.4%
E 49
11.4%
D 49
11.4%
49
11.4%
O 49
11.4%
F 49
11.4%
L 6
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 379
88.6%
Space Separator 49
 
11.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 67
17.7%
C 61
16.1%
H 49
12.9%
E 49
12.9%
D 49
12.9%
O 49
12.9%
F 49
12.9%
L 6
 
1.6%
Space Separator
ValueCountFrequency (%)
49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 379
88.6%
Common 49
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 67
17.7%
C 61
16.1%
H 49
12.9%
E 49
12.9%
D 49
12.9%
O 49
12.9%
F 49
12.9%
L 6
 
1.6%
Common
ValueCountFrequency (%)
49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 67
15.7%
C 61
14.3%
H 49
11.4%
E 49
11.4%
D 49
11.4%
49
11.4%
O 49
11.4%
F 49
11.4%
L 6
 
1.4%

TE_transDate
Date

Distinct683
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
Minimum1753-01-01 00:00:00
Maximum2021-07-12 00:00:00
2023-10-11T09:49:53.304451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-11T09:49:53.538976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TE_recDate
Date

Distinct564
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
Minimum1753-01-01 00:00:00
Maximum2021-07-12 00:00:00
2023-10-11T09:49:53.821762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-11T09:49:54.119894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Equipment No_
Text

MISSING 

Distinct130
Distinct (%)1.8%
Missing578
Missing (%)7.3%
Memory size61.9 KiB
2023-10-11T09:49:54.412167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.6110429
Min length2

Characters and Unicode

Total characters19152
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.3%

Sample

1st rowZZZ
2nd rowZZZ
3rd rowZZZ
4th rowZZZ
5th rowZZZ
ValueCountFrequency (%)
zzz 3475
47.2%
bl 390
 
5.3%
ch 249
 
3.4%
che 244
 
3.3%
tu 206
 
2.8%
toi 170
 
2.3%
cw 152
 
2.1%
whs 146
 
2.0%
fc 131
 
1.8%
ah 112
 
1.5%
Other values (117) 2082
28.3%
2023-10-11T09:49:54.753805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Z 10425
54.4%
C 1286
 
6.7%
H 867
 
4.5%
B 705
 
3.7%
L 662
 
3.5%
E 616
 
3.2%
W 573
 
3.0%
T 529
 
2.8%
F 504
 
2.6%
A 476
 
2.5%
Other values (18) 2509
 
13.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18965
99.0%
Decimal Number 165
 
0.9%
Space Separator 22
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 10425
55.0%
C 1286
 
6.8%
H 867
 
4.6%
B 705
 
3.7%
L 662
 
3.5%
E 616
 
3.2%
W 573
 
3.0%
T 529
 
2.8%
F 504
 
2.7%
A 476
 
2.5%
Other values (13) 2322
 
12.2%
Decimal Number
ValueCountFrequency (%)
1 133
80.6%
3 14
 
8.5%
4 10
 
6.1%
2 8
 
4.8%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18965
99.0%
Common 187
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 10425
55.0%
C 1286
 
6.8%
H 867
 
4.6%
B 705
 
3.7%
L 662
 
3.5%
E 616
 
3.2%
W 573
 
3.0%
T 529
 
2.8%
F 504
 
2.7%
A 476
 
2.5%
Other values (13) 2322
 
12.2%
Common
ValueCountFrequency (%)
1 133
71.1%
22
 
11.8%
3 14
 
7.5%
4 10
 
5.3%
2 8
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z 10425
54.4%
C 1286
 
6.7%
H 867
 
4.5%
B 705
 
3.7%
L 662
 
3.5%
E 616
 
3.2%
W 573
 
3.0%
T 529
 
2.8%
F 504
 
2.6%
A 476
 
2.5%
Other values (18) 2509
 
13.1%

Priority
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0520662
Minimum0
Maximum5
Zeros440
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-10-11T09:49:54.887839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median3
Q34
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1584824
Coefficient of variation (CV)0.37957316
Kurtosis0.78912014
Mean3.0520662
Median Absolute Deviation (MAD)1
Skewness-1.2582007
Sum24151
Variance1.3420815
MonotonicityNot monotonic
2023-10-11T09:49:55.032053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 3383
42.8%
3 2944
37.2%
1 664
 
8.4%
0 440
 
5.6%
2 429
 
5.4%
5 53
 
0.7%
ValueCountFrequency (%)
0 440
 
5.6%
1 664
 
8.4%
2 429
 
5.4%
3 2944
37.2%
4 3383
42.8%
5 53
 
0.7%
ValueCountFrequency (%)
5 53
 
0.7%
4 3383
42.8%
3 2944
37.2%
2 429
 
5.4%
1 664
 
8.4%
0 440
 
5.6%

Reason for Cancellation
Categorical

MISSING 

Distinct10
Distinct (%)27.0%
Missing7876
Missing (%)99.5%
Memory size61.9 KiB
Email: FW: MECHANICAL PR CLEAN UP 04//24/2019
24 
AS PER BQSANPEDRO / EMAIL SUBJECT: "FW: Electrical PR for Clean Up" DATED 03/06/2019
4 
EMAIL: FW: List of Items from ESD 04/26/2019
 
2
Email: FW: MECHANICAL PR CLEAN UP 04//24/2019 RE-PR SMPPR00742
 
1
Email: FW: MECHANICAL PR CLEAN UP 04//24/2019 RE-PR SMPPR00743
 
1
Other values (5)
5 

Length

Max length84
Median length45
Mean length49.189189
Min length9

Characters and Unicode

Total characters1820
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)18.9%

Sample

1st rowAS PER BQSANPEDRO / EMAIL SUBJECT: "FW: Electrical PR for Clean Up" DATED 03/06/2019
2nd rowAS PER BQSANPEDRO / EMAIL SUBJECT: "FW: Electrical PR for Clean Up" DATED 03/06/2019
3rd rowAS PER BQSANPEDRO / EMAIL SUBJECT: "FW: Electrical PR for Clean Up" DATED 03/06/2019
4th rowAS PER BQSANPEDRO / EMAIL SUBJECT: "FW: Electrical PR for Clean Up" DATED 03/06/2019
5th rowEMAIL: FW: List of Items from ESD 04/26/2019

Common Values

ValueCountFrequency (%)
Email: FW: MECHANICAL PR CLEAN UP 04//24/2019 24
 
0.3%
AS PER BQSANPEDRO / EMAIL SUBJECT: "FW: Electrical PR for Clean Up" DATED 03/06/2019 4
 
0.1%
EMAIL: FW: List of Items from ESD 04/26/2019 2
 
< 0.1%
Email: FW: MECHANICAL PR CLEAN UP 04//24/2019 RE-PR SMPPR00742 1
 
< 0.1%
Email: FW: MECHANICAL PR CLEAN UP 04//24/2019 RE-PR SMPPR00743 1
 
< 0.1%
Email: FW: MECHANICAL PR CLEAN UP 04//24/2019 RE-PR SMPPR00744 1
 
< 0.1%
Email: FW: MECHANICAL PR CLEAN UP 04//24/2019 RE-PR SMPPR00741 1
 
< 0.1%
Cancelled as per Amado warehouse 1
 
< 0.1%
Cancelled. Refer SMPPR00419 1
 
< 0.1%
Cancelled 1
 
< 0.1%
(Missing) 7876
99.5%

Length

2023-10-11T09:49:55.177209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:55.370385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
email 34
11.9%
fw 34
11.9%
pr 32
11.2%
clean 32
11.2%
up 32
11.2%
mechanical 28
9.8%
04//24/2019 28
9.8%
as 5
 
1.8%
per 5
 
1.8%
for 4
 
1.4%
Other values (22) 51
17.9%

Most occurring characters

ValueCountFrequency (%)
248
 
13.6%
E 116
 
6.4%
A 103
 
5.7%
/ 100
 
5.5%
C 95
 
5.2%
0 82
 
4.5%
P 82
 
4.5%
: 68
 
3.7%
2 65
 
3.6%
4 64
 
3.5%
Other values (41) 797
43.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 842
46.3%
Decimal Number 297
 
16.3%
Lowercase Letter 252
 
13.8%
Space Separator 248
 
13.6%
Other Punctuation 177
 
9.7%
Dash Punctuation 4
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 116
13.8%
A 103
12.2%
C 95
11.3%
P 82
9.7%
L 64
7.6%
N 60
 
7.1%
R 54
 
6.4%
M 39
 
4.6%
I 36
 
4.3%
U 36
 
4.3%
Other values (10) 157
18.6%
Lowercase Letter
ValueCountFrequency (%)
l 46
18.3%
a 42
16.7%
i 34
13.5%
m 33
13.1%
e 21
8.3%
r 13
 
5.2%
c 11
 
4.4%
o 10
 
4.0%
f 9
 
3.6%
t 8
 
3.2%
Other values (7) 25
9.9%
Decimal Number
ValueCountFrequency (%)
0 82
27.6%
2 65
21.9%
4 64
21.5%
1 36
12.1%
9 35
11.8%
6 6
 
2.0%
3 5
 
1.7%
7 4
 
1.3%
Other Punctuation
ValueCountFrequency (%)
/ 100
56.5%
: 68
38.4%
" 8
 
4.5%
. 1
 
0.6%
Space Separator
ValueCountFrequency (%)
248
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1094
60.1%
Common 726
39.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 116
 
10.6%
A 103
 
9.4%
C 95
 
8.7%
P 82
 
7.5%
L 64
 
5.9%
N 60
 
5.5%
R 54
 
4.9%
l 46
 
4.2%
a 42
 
3.8%
M 39
 
3.6%
Other values (27) 393
35.9%
Common
ValueCountFrequency (%)
248
34.2%
/ 100
13.8%
0 82
 
11.3%
: 68
 
9.4%
2 65
 
9.0%
4 64
 
8.8%
1 36
 
5.0%
9 35
 
4.8%
" 8
 
1.1%
6 6
 
0.8%
Other values (4) 14
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
248
 
13.6%
E 116
 
6.4%
A 103
 
5.7%
/ 100
 
5.5%
C 95
 
5.2%
0 82
 
4.5%
P 82
 
4.5%
: 68
 
3.7%
2 65
 
3.6%
4 64
 
3.5%
Other values (41) 797
43.8%

User ID
Text

MISSING 

Distinct80
Distinct (%)1.5%
Missing2559
Missing (%)32.3%
Memory size61.9 KiB
2023-10-11T09:49:55.944564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.36328
Min length15

Characters and Unicode

Total characters98317
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowSEMCALACA\EDVELASCO
2nd rowSEMCALACA\EDVELASCO
3rd rowSEMCALACA\EDVELASCO
4th rowSEMCALACA\EDVELASCO
5th rowSEMCALACA\EDVELASCO
ValueCountFrequency (%)
semcalaca\mjespera 1256
23.5%
semcalaca\jbescalona 377
 
7.0%
semcalaca\edvelasco 310
 
5.8%
semcalaca\agguevarra 269
 
5.0%
semcalaca\rgviolante 232
 
4.3%
semcalaca\hnmayor 212
 
4.0%
semcalaca\mfnadal 196
 
3.7%
semcalaca\rvendaya 156
 
2.9%
semcalaca\mmbuno 154
 
2.9%
semcalaca\gamatira 143
 
2.7%
Other values (70) 2049
38.3%
2023-10-11T09:49:56.610901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 23283
23.7%
C 12159
12.4%
E 9849
10.0%
M 8400
 
8.5%
S 7839
 
8.0%
L 7822
 
8.0%
\ 5354
 
5.4%
R 3594
 
3.7%
N 3136
 
3.2%
O 3037
 
3.1%
Other values (14) 13844
14.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 92963
94.6%
Other Punctuation 5354
 
5.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 23283
25.0%
C 12159
13.1%
E 9849
10.6%
M 8400
 
9.0%
S 7839
 
8.4%
L 7822
 
8.4%
R 3594
 
3.9%
N 3136
 
3.4%
O 3037
 
3.3%
J 2082
 
2.2%
Other values (13) 11762
12.7%
Other Punctuation
ValueCountFrequency (%)
\ 5354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92963
94.6%
Common 5354
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 23283
25.0%
C 12159
13.1%
E 9849
10.6%
M 8400
 
9.0%
S 7839
 
8.4%
L 7822
 
8.4%
R 3594
 
3.9%
N 3136
 
3.4%
O 3037
 
3.3%
J 2082
 
2.2%
Other values (13) 11762
12.7%
Common
ValueCountFrequency (%)
\ 5354
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 23283
23.7%
C 12159
12.4%
E 9849
10.0%
M 8400
 
8.5%
S 7839
 
8.0%
L 7822
 
8.0%
\ 5354
 
5.4%
R 3594
 
3.7%
N 3136
 
3.2%
O 3037
 
3.1%
Other values (14) 13844
14.1%

Plant No_
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
3
6507 
0
 
537
1
 
455
2
 
414

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7913
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row0
4th row3
5th row0

Common Values

ValueCountFrequency (%)
3 6507
82.2%
0 537
 
6.8%
1 455
 
5.8%
2 414
 
5.2%

Length

2023-10-11T09:49:56.774747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:56.987238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 6507
82.2%
0 537
 
6.8%
1 455
 
5.8%
2 414
 
5.2%

Most occurring characters

ValueCountFrequency (%)
3 6507
82.2%
0 537
 
6.8%
1 455
 
5.8%
2 414
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 6507
82.2%
0 537
 
6.8%
1 455
 
5.8%
2 414
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 7913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 6507
82.2%
0 537
 
6.8%
1 455
 
5.8%
2 414
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 6507
82.2%
0 537
 
6.8%
1 455
 
5.8%
2 414
 
5.2%

MP Work Order No_
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7913
Missing (%)100.0%
Memory size61.9 KiB

Reference MP PR No_
Text

MISSING 

Distinct940
Distinct (%)100.0%
Missing6973
Missing (%)88.1%
Memory size61.9 KiB
2023-10-11T09:49:57.414402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters9400
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique940 ?
Unique (%)100.0%

Sample

1st rowSMPR006229
2nd rowSMPR006237
3rd rowSMPR006247
4th rowSMPR006254
5th rowSMPR006255
ValueCountFrequency (%)
smpr013759 1
 
0.1%
smpr006355 1
 
0.1%
smpr006638 1
 
0.1%
smpr006237 1
 
0.1%
smpr006247 1
 
0.1%
smpr006254 1
 
0.1%
smpr006255 1
 
0.1%
smpr006256 1
 
0.1%
smpr006258 1
 
0.1%
smpr006259 1
 
0.1%
Other values (930) 930
98.9%
2023-10-11T09:49:58.471175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1607
17.1%
1 1011
10.8%
S 940
10.0%
M 940
10.0%
P 940
10.0%
R 940
10.0%
9 421
 
4.5%
6 416
 
4.4%
4 412
 
4.4%
5 371
 
3.9%
Other values (4) 1402
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5640
60.0%
Uppercase Letter 3760
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1607
28.5%
1 1011
17.9%
9 421
 
7.5%
6 416
 
7.4%
4 412
 
7.3%
5 371
 
6.6%
8 365
 
6.5%
7 351
 
6.2%
2 349
 
6.2%
3 337
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
S 940
25.0%
M 940
25.0%
P 940
25.0%
R 940
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5640
60.0%
Latin 3760
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1607
28.5%
1 1011
17.9%
9 421
 
7.5%
6 416
 
7.4%
4 412
 
7.3%
5 371
 
6.6%
8 365
 
6.5%
7 351
 
6.2%
2 349
 
6.2%
3 337
 
6.0%
Latin
ValueCountFrequency (%)
S 940
25.0%
M 940
25.0%
P 940
25.0%
R 940
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1607
17.1%
1 1011
10.8%
S 940
10.0%
M 940
10.0%
P 940
10.0%
R 940
10.0%
9 421
 
4.5%
6 416
 
4.4%
4 412
 
4.4%
5 371
 
3.9%
Other values (4) 1402
14.9%

PR Item Category
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
0
7878 
1
 
20
3
 
12
4
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7913
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7878
99.6%
1 20
 
0.3%
3 12
 
0.2%
4 2
 
< 0.1%
2 1
 
< 0.1%

Length

2023-10-11T09:49:58.791622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:59.039484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7878
99.6%
1 20
 
0.3%
3 12
 
0.2%
4 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7878
99.6%
1 20
 
0.3%
3 12
 
0.2%
4 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7878
99.6%
1 20
 
0.3%
3 12
 
0.2%
4 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 7913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7878
99.6%
1 20
 
0.3%
3 12
 
0.2%
4 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7878
99.6%
1 20
 
0.3%
3 12
 
0.2%
4 2
 
< 0.1%
2 1
 
< 0.1%

Role Center Status
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1301656
Minimum0
Maximum5
Zeros3864
Zeros (%)48.8%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-10-11T09:49:59.145903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1237001
Coefficient of variation (CV)0.99696483
Kurtosis-1.8814226
Mean2.1301656
Median Absolute Deviation (MAD)2
Skewness0.057998027
Sum16856
Variance4.5101023
MonotonicityNot monotonic
2023-10-11T09:49:59.236929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3864
48.8%
4 2950
37.3%
5 950
 
12.0%
2 137
 
1.7%
3 10
 
0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
0 3864
48.8%
1 2
 
< 0.1%
2 137
 
1.7%
3 10
 
0.1%
4 2950
37.3%
5 950
 
12.0%
ValueCountFrequency (%)
5 950
 
12.0%
4 2950
37.3%
3 10
 
0.1%
2 137
 
1.7%
1 2
 
< 0.1%
0 3864
48.8%

No_ Series
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7913
Missing (%)100.0%
Memory size61.9 KiB

PR Acceptance Date
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
-53688
7913 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters47478
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-53688
2nd row-53688
3rd row-53688
4th row-53688
5th row-53688

Common Values

ValueCountFrequency (%)
-53688 7913
100.0%

Length

2023-10-11T09:49:59.353825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:59.456491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
53688 7913
100.0%

Most occurring characters

ValueCountFrequency (%)
8 15826
33.3%
- 7913
16.7%
5 7913
16.7%
3 7913
16.7%
6 7913
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39565
83.3%
Dash Punctuation 7913
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 15826
40.0%
5 7913
20.0%
3 7913
20.0%
6 7913
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 7913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47478
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 15826
33.3%
- 7913
16.7%
5 7913
16.7%
3 7913
16.7%
6 7913
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 15826
33.3%
- 7913
16.7%
5 7913
16.7%
3 7913
16.7%
6 7913
16.7%

PR Approving Status
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15531404
Minimum0
Maximum10
Zeros7722
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-10-11T09:49:59.552026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0641767
Coefficient of variation (CV)6.8517741
Kurtosis48.202724
Mean0.15531404
Median Absolute Deviation (MAD)0
Skewness7.0198554
Sum1229
Variance1.1324721
MonotonicityNot monotonic
2023-10-11T09:49:59.650377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 7722
97.6%
8 129
 
1.6%
1 20
 
0.3%
4 14
 
0.2%
3 9
 
0.1%
2 6
 
0.1%
6 6
 
0.1%
5 4
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 7722
97.6%
1 20
 
0.3%
2 6
 
0.1%
3 9
 
0.1%
4 14
 
0.2%
5 4
 
0.1%
6 6
 
0.1%
7 1
 
< 0.1%
8 129
 
1.6%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 129
1.6%
7 1
 
< 0.1%
6 6
 
0.1%
5 4
 
0.1%
4 14
 
0.2%
3 9
 
0.1%
2 6
 
0.1%
1 20
 
0.3%

PR Type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
0
7765 
2
 
84
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7913
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7765
98.1%
2 84
 
1.1%
1 64
 
0.8%

Length

2023-10-11T09:49:59.746502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T09:49:59.839055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7765
98.1%
2 84
 
1.1%
1 64
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 7765
98.1%
2 84
 
1.1%
1 64
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7765
98.1%
2 84
 
1.1%
1 64
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 7913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7765
98.1%
2 84
 
1.1%
1 64
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7765
98.1%
2 84
 
1.1%
1 64
 
0.8%

Missing values

2023-10-11T09:49:38.362758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-11T09:49:39.402567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-11T09:49:39.867397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

timestampNo_PR DatePrinting DateDepartmentIntended ForPhase of WorkReferenceWork DescriptionDate NeededBatchTemplateExternal PR No_StatusReOpenedRelease DateRemarksDimension Set IDDoc_ No_ OccurrencePrintedDate PR ReceivedPR Monitoring StatusPurchaser CodeLocation CodeTE_transDateTE_recDateEquipment No_PriorityReason for CancellationUser IDPlant No_MP Work Order No_Reference MP PR No_PR Item CategoryRole Center StatusNo_ SeriesPR Acceptance DatePR Approving StatusPR Type
0000000000FD66B8CBRPR-000032014-10-28-53688150.02X150MW POWER PLANTNaNBILLING INVOICENaN2014-10-31NaNREQ.NaN112014-10-29SLBPO-000028 OCEAN STAR FREIGHT EXPRESS0102014-10-2810HSMNaN1753-01-011753-01-01NaN0NaNNaN3NaNNaN02NaN-5368800
1000000000FD66B8DBRPR-000042014-10-28-53688150.02X150MW POWER PLANTNaNATTACHED BROKER SOANaN1753-01-01NaNREQ.NaN112014-10-29SLBPO-000002 AMERICAN FREIGHT FORWARDERS, INC.0102014-10-2810HSMNaN1753-01-011753-01-01NaN0NaNNaN0NaNNaN04NaN-5368800
2000000000FD66B8EBRPR-000052014-10-28-53688150.02X150MW POWER PLANTNaNATTACHED BROKER SOANaN1753-01-01NaNREQ.NaN112014-10-29SLBPO-000003 CARGO PLUS FREIGHT EXPRESS0102014-10-2810HSMNaN1753-01-011753-01-01NaN0NaNNaN0NaNNaN04NaN-5368800
3000000000FD66B8FBRPR-000062014-10-28-53688150.02X150MW POWER PLANTNaNATTACHED BILLING STATEMENTNaN1753-01-01NaNREQ.NaN112014-10-29SLBPO-000020 GCLPI-GATEWAY CONTAINER LINE PHILS. INC.0102014-10-2810HSMNaN1753-01-011753-01-01NaN0NaNNaN3NaNNaN02NaN-5368800
4000000000FD66B90BRPR-000072014-10-28-53688150.02X150MW POWER PLANTNaNATTACHED BROKER'S BILLINGNaN1753-01-01NaNREQ.NaN112014-10-30SLBPO-000019 CARGO PLUS FREIGHT EXPRESS0102014-10-2810HSMNaN1753-01-011753-01-01NaN0NaNNaN0NaNNaN00NaN-5368800
5000000000FD66B91BRPR-000082014-10-28-53688150.02X150MW POWER PLANTNaNATTACHED BROKER'S SOANaN1753-01-01NaNREQ.NaN112014-10-29SLBPO-000018 CARGO PLUS FREIGHT EXPRESS0102014-10-2810HSMNaN1753-01-011753-01-01NaN0NaNNaN3NaNNaN04NaN-5368800
6000000000FD66B92BRPR-000092014-10-28-53688150.02X150M POWER PLANTNaNATTACHED BROKER'S SOANaN1753-01-01NaNREQ.NaN112014-10-30SLBPO-000017 CARGO PLUS FREIGHT EXPRESS0102014-10-2810HSMNaN1753-01-011753-01-01NaN0NaNNaN0NaNNaN04NaN-5368800
7000000000FD66B93BRPR-000102014-10-29-53688150.02X150MW POWER PLANTNaNATTACHED BROKER'S BILLING INVOICENaN2014-10-31NaNREQ.NaN112014-10-30SLBPO-000016 CARGO PLUS FREIGHT EXPRESS0102014-10-2910HSMNaN1753-01-011753-01-01NaN0NaNNaN3NaNNaN04NaN-5368800
8000000000FD66B94BRPR-000112014-10-29-53688150.02X150MW POWER PLANTNaNATTACHED BILLING INVOICENaN1753-01-01NaNREQ.NaN112014-10-29SLBPO-000015 CARGO PLUS FREIGHT EXPRESS0102014-10-2910HSMNaN1753-01-011753-01-01NaN0NaNNaN3NaNNaN04NaN-5368800
9000000000FD66B95BRPR-000122014-10-29-53688150.02X150MW POWER PLANTNaNATTACHED BILLING INVOICENaN1753-01-01NaNREQ.NaN112014-10-30SLBPO-000014 CARGO PLUS FREIGHT EXPRESS0102014-10-2910HSMNaN1753-01-011753-01-01NaN0NaNNaN3NaNNaN04NaN-5368800
timestampNo_PR DatePrinting DateDepartmentIntended ForPhase of WorkReferenceWork DescriptionDate NeededBatchTemplateExternal PR No_StatusReOpenedRelease DateRemarksDimension Set IDDoc_ No_ OccurrencePrintedDate PR ReceivedPR Monitoring StatusPurchaser CodeLocation CodeTE_transDateTE_recDateEquipment No_PriorityReason for CancellationUser IDPlant No_MP Work Order No_Reference MP PR No_PR Item CategoryRole Center StatusNo_ SeriesPR Acceptance DatePR Approving StatusPR Type
7903000000000FD689DDTDCPR-00042016-02-22-53688845.0DOCUMENT MANAGEMENT SYSTEMNaNNaNNaN2016-04-30NaNREQ.NaN001753-01-01ANNUAL MAINTENANCE AGREEMENT OF DOCUMENTUM0102016-02-2217JCCNaN1753-01-011753-01-01ZZZ4NaNNaN3NaNNaN00NaN-5368800
7904000000000FD689DETDCPR-00052017-03-14-53688845.0IMS TEAMIMS TEAM UNIFORMNaNNaN2017-04-03NaNREQ.NaN112017-03-23SLPO-001928 Seine garments corp.0102017-03-1410JCCNaN1753-01-011753-01-01ZZZ4NaNSEMCALACA\GALLAM3NaNNaN00NaN-5368800
7905000000000FD689DFTDCPR-00062018-03-08-53688845.0FOR VARIOUS MEETINGSNaNNaNNaN2018-04-02NaNREQ.NaN112018-03-13SLPO-002918 ANDELAI GENERAL MERCHANDISE, INC. /SLPO-002919 E-PLUS STATIONERY, INC. /SLPO-002920 SQUARE DEAL OFFICE RESOURCES INC.5481102018-03-1310PBBNaN2018-03-222018-04-04ZZZ3NaNSEMCALACA\GALLAM3NaNNaN00NaN-5368800
7906000000000FD689E0TDCPR-00072018-03-09-53688845.0TO BE USED FOR THE NEW COMPANYNaNNaNNaN2018-03-26NaNREQ.NaN112018-03-16SLPO-003008 TRANSGLOBE INDUSTRIAL5481102018-03-0910PBBNaN2018-04-062018-04-18ZZZ3NaNSEMCALACA\GALLAM3NaNNaN04NaN-5368800
7907000000000FD689E1TDCPR-00082018-03-12-53688845.0IMS/TDC OFFICETRANSFER OF OFFICENaNMODIFICATION OF TDC/IMS OFFICE2018-04-02NaNREQ.NaN112018-03-15AS PER KBSJ5481102018-03-1511IGMNaN1753-01-011753-01-01ZZZ4NaNSEMCALACA\GALLAM3NaNNaN04NaN-5368800
7908000000000FD689E2TDCPR-00092018-05-31-53688845.0IMS INCENTIVE AWARDGHG TEAM INCENTIVENaNNaN2018-06-15NaNREQ.NaN112018-06-16SLPO-003228 SEINE GARMENTS CORP.5481102018-06-1610PBBNaN2018-07-252018-08-02ZZZ4NaNSEMCALACA\GALLAM3NaNNaN00NaN-5368800
7909000000000FD689E3TDCPR-00102018-08-07-53688845.0ANNUAL OPERATIONS PLANNINGPLANNINGNaNNaN2018-08-31NaNREQ.NaN112018-08-31NEEDED PRIOR TO SCHEDULED PLANNING ON SEPT. 7-8 /SLPO-003295 SEINE GARMENTS CORP.5481102018-08-1410PBBNaN1753-01-011753-01-01ZZZ4NaNSEMCALACA\GALLAM3NaNNaN04NaN-5368800
7910000000000FD689E4TDCPR-00112019-04-10-53688845.0TECHNICAL DOCUMENTATION (MANUALS)TECHNICAL DOCUMENTATION DIGITIZATION /MODIFICATIONNaNDIGITIZATION OF MANUALS2019-04-30NaNREQ.NaN012019-05-15As per Sir RVE cancelled this PR thru email dated 8/27/20205481102019-05-1611MMMNaN2019-07-311753-01-01ZZZ4NaNSEMCALACA\GALLAM3NaNNaN04NaN-5368800
7911000000000FD689E5TDCPR-00132020-02-26-53688848.0DIGITIZATION PROJECTCENTRALIZATION OF TECHNICAL DOCUMENTSOTP 2020 / BUDGET CAPEX 2020SCANNING AND PHOTOCOPYING OF TECH. DOC.2020-03-26NaNREQ.NaN112020-03-06SLPO-004645 Tricom Dynamics / SLPO-004646 PARAMOUNT43930102020-03-0910AFRNaN2020-06-182020-06-24ZZZ4NaNSEMCALACA\JBDELACRUZ3NaNNaN05NaN-5368800
7912000000000FD689E6TDCPR-00142020-03-02-53688848.0Asset Management - TDCCentralization of technical documentsNaNCAD Servicing of TDC technical drawings2020-04-01NaNREQ.NaN112020-05-11NaN43930102020-05-213ABMNaN2020-06-231753-01-01ZZZ4NaNSEMCALACA\JBDELACRUZ3NaNNaN04NaN-5368800